diff --git a/wheel-defect-detection/README.dataset.txt b/wheel-defect-detection/README.dataset.txt
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+# wheel-defect-detection-e53jb-38chk-ytwg > 2025-03-13 8:06am
+https://universe.roboflow.com/rf-100-vl/wheel-defect-detection-e53jb-38chk-ytwg
+
+Provided by a Roboflow user
+License: MIT
+
+# Overview
+- [Introduction](#introduction)
+- [Object Classes](#object-classes)
+  - [Cracks-Scratches](#cracks-scratches)
+  - [Discoloration](#discoloration)
+  - [Shelling](#shelling)
+  - [Wheel](#wheel)
+
+# Introduction
+This dataset is designed to detect and analyze defects in wheels. It includes four main classes to help identify potential issues that could affect performance and safety.
+
+Classes:
+- **Cracks-Scratches**: Visible lines or marks on the wheel that indicate damage.
+- **Discoloration**: Areas where the wheel surface shows abnormal color changes.
+- **Shelling**: Parts of the wheel surface that appear pitted or hollowed out.
+- **Wheel**: The entire wheel structure.
+
+# Object Classes
+
+## Cracks-Scratches
+### Description
+Lines or seams that appear on the wheel’s surface, often consistent and sometimes deep or shallow.
+
+### Instructions
+- Annotate visible lines and marks that trace across the wheel’s surface.
+- The annotations should capture the full extent of each line.
+- Do not include smudges or dirt that do not have defined linear shapes.
+
+## Discoloration
+### Description
+Patches or areas on the wheel where the color differs from the rest of the material, indicating potential corrosion or fading.
+
+### Instructions
+- Focus on areas where the color noticeably changes, usually darker or lighter than the surrounding region.
+- Exclude regions where lighting changes cause temporary shadows or highlights.
+
+## Shelling
+### Description
+Areas on the wheel surface that are hollowed or indented, resembling small craters or pits.
+
+### Instructions
+- Outline regions that appear to be missing material, forming depressions on the wheel.
+- Avoid marking areas that are merely dirty or have surface residue but are not structurally hollowed.
+
+## Wheel
+### Description
+The entire circular structure, including spokes and hubs, typically made of metal or alloy.
+
+### Instructions
+- Locate and enclose the entire wheel, ensuring that the edges of the wheel are precisely defined.
+- Include all visible parts of the wheel, but do not extend the annotation over occluding objects such as nearby parts or machinery.
\ No newline at end of file
diff --git a/wheel-defect-detection/README.roboflow.txt b/wheel-defect-detection/README.roboflow.txt
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+
+wheel-defect-detection-e53jb-38chk-ytwg - v1 2025-03-13 8:06am
+==============================
+
+This dataset was exported via roboflow.com on March 14, 2025 at 6:27 AM GMT
+
+Roboflow is an end-to-end computer vision platform that helps you
+* collaborate with your team on computer vision projects
+* collect & organize images
+* understand and search unstructured image data
+* annotate, and create datasets
+* export, train, and deploy computer vision models
+* use active learning to improve your dataset over time
+
+For state of the art Computer Vision training notebooks you can use with this dataset,
+visit https://github.com/roboflow/notebooks
+
+To find over 100k other datasets and pre-trained models, visit https://universe.roboflow.com
+
+The dataset includes 309 images.
+Wheel-defect-detection-e53jb-38chk-ytwg are annotated in COCO format.
+
+The following pre-processing was applied to each image:
+
+No image augmentation techniques were applied.
+
+
diff --git a/wheel-defect-detection/test/_annotations.coco.json b/wheel-defect-detection/test/_annotations.coco.json
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index 0000000000000000000000000000000000000000..a99b19ab97ee8f8e86056c460a87f4af4052dc05
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+++ b/wheel-defect-detection/test/_annotations.coco.json
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